Named Entity Recognition using Hundreds of Thousands of Features

We present an approach to named entity recognition that uses support vector machines to capture transition probabilities in a lattice. The support vector machines are trained with hundreds of thousands of features drawn from the CoNLL-2003 Shared Task training data. Margin outputs are converted to estimated probabilities using a simple static function… CONTINUE READING

3 Figures & Tables

Topics

Statistics

051015'04'06'08'10'12'14'16'18
Citations per Year

104 Citations

Semantic Scholar estimates that this publication has 104 citations based on the available data.

See our FAQ for additional information.